hw05
Yeeun Lee
Exercise 1: Explain the value of the here::here package
“here::here()” provides a simple way to detect the root directory as well as set a working directory. What I like about “here” package most is that I can easily detect root directory of a file or data or work with paths, and those do not need to correspond to the working directory. That is the big difference between ‘hear’ and ‘setwd’ or ‘getwd’. Moreover, by setting relative paths, users do not need to specify all paths explicitly, which makes R project accessible in a platform-independent way.
Exercise 2: Factor management
Step1. Selecting datase: gapminder
Step 2: Check levels I will first check (1) whether continent is factor or character and (2) what are the levels
class(gapminder$continent)## [1] "factor"levels(gapminder$continent)## [1] "Africa" "Americas" "Asia" "Europe" "Oceania"Step 3: Drop “Oceania”"
gap_drop_oceania <- gapminder %>%
filter(!continent == "Oceania")
gap_drop_oceania %>%
droplevels()## # A tibble: 1,680 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # … with 1,670 more rowsgap_drop_oceania## # A tibble: 1,680 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # … with 1,670 more rowsStep 4: Reorder the levels of country or continent. I will select 4 East Asia countries and reorder the levels of country by gdpPercap.
eastasia <- c("Japan", "Korea, Rep.", "China", "Korea, Dem. Rep.")
east_asia <- gapminder %>%
filter(country %in% eastasia)
ggplot(east_asia, aes(x=year, y= gdpPercap,
color = fct_reorder2(country, year, gdpPercap))) +
geom_line()+
labs (color="country")
Exercise 3: File input/output (I/O)
Step 1. Export the East_asian file
write_csv(east_asia, here("east_asia.csv"))Step 2. Import the file again.
read_csv(here::here("east_asia.csv"))Exercise 4: Visualization design
Step 1: Selecting one previous plot: (hw 03)How is life expectancy changing over time on different continents?
plot1 <- gapminder %>%
group_by(continent, year) %>%
mutate(life_expectancy = mean(lifeExp)) %>%
ggplot(aes(year, life_expectancy, colour = continent)) + geom_line(alpha = 0.4)
plot1
Step 2: Revising the plot: chaging the labels; adding the title; making it interactive using plotly package
plot2 <- gapminder %>%
group_by(continent, year) %>%
mutate(life_expectancy = mean(lifeExp)) %>%
ggplot(aes(year, life_expectancy, colour = continent)) +
geom_line(alpha = 0.4) +
labs(title="Life expectancy changes by continent", x="Year", y="Life expectancy") +
theme(panel.grid.minor = element_blank())
plot2 %>%
ggplotly()Step 3: Placing these plots side-by-side:
(plot_comparison<- gridExtra::grid.arrange(plot1, plot2, ncol = 2))
## TableGrob (1 x 2) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]Exercise 5: Writing figures to file
ggsave("life_expectancy.png", plot = plot_comparison, width = 20, height = 10, dpi = 300)